Examples¶
This notebook shows some examples of how to use the recommendation server.
Setup¶
[1]:
import requests
import json
Let’s define some variable we are going to use in the examples
[2]:
rec_server_base_url = "http://127.0.0.1:5000/"
rec_algos = ["popular"]
pred_algos = ["bias","itemitem","useruser","biasedmf","implicitmf","funksvd","tf_bpr"]
user_id = 1
nr_recs = 10
items = "10,20,30,40,50"
Recommendations¶
[3]:
for algo in rec_algos:
url = f'{rec_server_base_url}/algorithms/{algo}/recommendations?user_id={user_id}&num_recs={nr_recs}'
r = requests.get(url)
print(json.dumps(r.json(), indent=2))
{
"recommendations": [
{
"item": 356.0,
"score": 341.0
},
{
"item": 318.0,
"score": 311.0
},
{
"item": 593.0,
"score": 304.0
},
{
"item": 260.0,
"score": 291.0
},
{
"item": 480.0,
"score": 274.0
},
{
"item": 2571.0,
"score": 259.0
},
{
"item": 1.0,
"score": 247.0
},
{
"item": 527.0,
"score": 244.0
},
{
"item": 589.0,
"score": 237.0
},
{
"item": 1196.0,
"score": 234.0
}
]
}
Predictions¶
[4]:
for algo in pred_algos:
print(f"Algorithm: {algo}")
url = f'{rec_server_base_url}/algorithms/{algo}/predictions?user_id={user_id}&items={items}'
r = requests.get(url)
print(json.dumps(r.json(), indent=2))
print()
print('-----------------------------')
print()
Algorithm: bias
{
"predictions": [
{
"item": 10,
"score": 3.4674757437886936
},
{
"item": 20,
"score": 2.5551176101190847
},
{
"item": 30,
"score": 4.066656071657546
},
{
"item": 40,
"score": 3.933322738324213
},
{
"item": 50,
"score": 4.387302837826703
}
]
}
-----------------------------
Algorithm: itemitem
{
"predictions": [
{
"item": 10,
"score": 3.231073190470284
},
{
"item": 20,
"score": 2.9305180210428925
},
{
"item": 30,
"score": 4.073950231233046
},
{
"item": 40,
"score": 3.986381186580402
},
{
"item": 50,
"score": 4.580127129467687
}
]
}
-----------------------------
Algorithm: useruser
{
"predictions": [
{
"item": 10,
"score": 3.4913655291780747
},
{
"item": 20,
"score": 3.516813277855599
},
{
"item": 30,
"score": 4.204543778213467
},
{
"item": 40,
"score": 4.672953331143783
},
{
"item": 50,
"score": 4.4831597852166025
}
]
}
-----------------------------
Algorithm: biasedmf
{
"predictions": [
{
"item": 10,
"score": 3.2654584201076498
},
{
"item": 20,
"score": 3.20231997852298
},
{
"item": 30,
"score": 4.129488327175242
},
{
"item": 40,
"score": 3.6586054841117126
},
{
"item": 50,
"score": 4.600072996787447
}
]
}
-----------------------------
Algorithm: implicitmf
{
"predictions": [
{
"item": 10,
"score": 0.7739809836712266
},
{
"item": 20,
"score": -0.14057957568765025
},
{
"item": 30,
"score": 0.30112599732114315
},
{
"item": 40,
"score": 0.150151834788069
},
{
"item": 50,
"score": 0.9581236325369329
}
]
}
-----------------------------
Algorithm: funksvd
{
"predictions": [
{
"item": 10,
"score": 2.763367031201388
},
{
"item": 20,
"score": 2.16933683430268
},
{
"item": 30,
"score": 3.238171076963463
},
{
"item": 40,
"score": 3.1055437883876067
},
{
"item": 50,
"score": 3.635343170703555
}
]
}
-----------------------------
Algorithm: tf_bpr
{
"predictions": [
{
"item": 10,
"score": 4.359846115112305
},
{
"item": 20,
"score": 1.9790164232254028
},
{
"item": 30,
"score": 1.851624608039856
},
{
"item": 40,
"score": 1.1883563995361328
},
{
"item": 50,
"score": 6.511105537414551
}
]
}
-----------------------------
Get model information¶
[5]:
for algo in pred_algos:
print(f"Algorithm: {algo}")
url = f'{rec_server_base_url}/algorithms/{algo}/info'
r = requests.get(url)
print(json.dumps(r.json(), indent=2))
print()
print('-----------------------------')
print()
Algorithm: bias
{
"model": {
"creation_date": "2020-10-19 23:42:14",
"size": 739.388,
"updated_date": "2020-10-19 23:42:14"
}
}
-----------------------------
Algorithm: itemitem
{
"model": {
"creation_date": "2020-10-21 16:47:08",
"size": 212330.473,
"updated_date": "2020-10-21 16:47:08"
}
}
-----------------------------
Algorithm: useruser
{
"model": {
"creation_date": "2020-10-15 21:16:28",
"size": 688.368,
"updated_date": "2020-08-21 18:33:27"
}
}
-----------------------------
Algorithm: biasedmf
{
"model": {
"creation_date": "2020-10-15 21:16:28",
"size": 3973.614,
"updated_date": "2020-10-08 21:10:51"
}
}
-----------------------------
Algorithm: implicitmf
{
"model": {
"creation_date": "2020-10-15 21:16:28",
"size": 1507.69,
"updated_date": "2020-08-21 18:33:45"
}
}
-----------------------------
Algorithm: funksvd
{
"model": {
"creation_date": "2020-10-15 21:16:28",
"size": 1522.498,
"updated_date": "2020-08-21 18:33:49"
}
}
-----------------------------
Algorithm: tf_bpr
{
"model": {
"creation_date": "2020-10-27 17:25:43",
"size": 19515.322,
"updated_date": "2020-10-27 17:25:43"
}
}
-----------------------------
Upload a model file¶
[6]:
current_algo = rec_algos[0]
url = f'{rec_server_base_url}/algorithms/{current_algo}/modelfile'
model_name = current_algo + ".bpk"
files = {
'file': open(model_name, 'rb')
}
response = requests.put(url, files=files)
print(json.dumps(r.json(), indent=2))
{
"model": {
"creation_date": "2020-10-27 17:25:43",
"size": 19515.322,
"updated_date": "2020-10-27 17:25:43"
}
}
Extending the recommendation server¶
An example of extending the recommendation server:
Create a new custom endpoint where we return the worst predictions for the algo sent in the request.
We created a new method called get_worst_predictions_from_model in lkweb/model_manager.py and another endpoint called get_worst_predictions with the python decorator: @models.model_method(“worst_predictions”, Predictor, models.get_worst_predictions_from_model, models.get_preds_params) in app.py
And that’s it, we have a new endpoint that returns the worst predictions for an algorithm. The code is shown below:
[7]:
def get_worst_predictions_from_model(self, model, *args):
user, items = None, None
try:
user, items, ratings = args[0][0], args[0][1], args[0][2]
results = []
df_preds = model.predict_for_user(user, items, ratings)
for index, value in df_preds.iteritems():
if not math.isnan(value):
results.append({'item': index, 'score': value})
results = sorted(results, key = lambda i: i['score'])
return results
except:
logging.error(f"Unexpected preds error for user: {user}, with items: {items}. Error: {sys.exc_info()[0]}")
raise
[8]:
def get_worst_predictions(results):
"""
Get worst predictions using the algorithm, user id and items sent.
Args:
algo: algorithm to be used.
user_id: user id to get predictions for.
items: items to get predictions for.
Returns:
A list of predictions with items and scores.
"""
return jsonify({"predictions": results})
Let’s test the new endpoint.
[9]:
for algo in pred_algos:
print(f"Algorithm: {algo}")
url = f'{rec_server_base_url}/algorithms/{algo}/worst_predictions?user_id={user_id}&items={items}'
r = requests.get(url)
print(json.dumps(r.json(), indent=2))
print()
print('-----------------------------')
print()
Algorithm: bias
{
"predictions": [
{
"item": 20,
"score": 2.5551176101190847
},
{
"item": 10,
"score": 3.4674757437886936
},
{
"item": 40,
"score": 3.933322738324213
},
{
"item": 30,
"score": 4.066656071657546
},
{
"item": 50,
"score": 4.387302837826703
}
]
}
-----------------------------
Algorithm: itemitem
{
"predictions": [
{
"item": 20,
"score": 2.9305180210428925
},
{
"item": 10,
"score": 3.231073190470284
},
{
"item": 40,
"score": 3.986381186580402
},
{
"item": 30,
"score": 4.073950231233046
},
{
"item": 50,
"score": 4.580127129467687
}
]
}
-----------------------------
Algorithm: useruser
{
"predictions": [
{
"item": 10,
"score": 3.4913655291780747
},
{
"item": 20,
"score": 3.516813277855599
},
{
"item": 30,
"score": 4.204543778213467
},
{
"item": 50,
"score": 4.4831597852166025
},
{
"item": 40,
"score": 4.672953331143783
}
]
}
-----------------------------
Algorithm: biasedmf
{
"predictions": [
{
"item": 20,
"score": 3.20231997852298
},
{
"item": 10,
"score": 3.2654584201076498
},
{
"item": 40,
"score": 3.6586054841117126
},
{
"item": 30,
"score": 4.129488327175242
},
{
"item": 50,
"score": 4.600072996787447
}
]
}
-----------------------------
Algorithm: implicitmf
{
"predictions": [
{
"item": 20,
"score": -0.14057957568765025
},
{
"item": 40,
"score": 0.150151834788069
},
{
"item": 30,
"score": 0.30112599732114315
},
{
"item": 10,
"score": 0.7739809836712266
},
{
"item": 50,
"score": 0.9581236325369329
}
]
}
-----------------------------
Algorithm: funksvd
{
"predictions": [
{
"item": 20,
"score": 2.16933683430268
},
{
"item": 10,
"score": 2.763367031201388
},
{
"item": 40,
"score": 3.1055437883876067
},
{
"item": 30,
"score": 3.238171076963463
},
{
"item": 50,
"score": 3.635343170703555
}
]
}
-----------------------------
Algorithm: tf_bpr
{
"predictions": [
{
"item": 40,
"score": 1.1883563995361328
},
{
"item": 30,
"score": 1.851624608039856
},
{
"item": 20,
"score": 1.9790164232254028
},
{
"item": 10,
"score": 4.359846115112305
},
{
"item": 50,
"score": 6.511105537414551
}
]
}
-----------------------------
[ ]: